How Many Rays Do I Need for Monte Carlo Optimization? "j=E8Dd}
While it is important to ensure that a sufficient number of rays are traced to QM { B(zH
distinguish the merit function value from the noise floor, it is often not necessary to Sre:l'.
trace as many rays during optimization as you might to obtain a given level of
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accuracy for analysis purposes. What matters during optimization is that the /Y [ b8f
changes the optimizer makes to the model affect the merit function in the same way xN6}4JB
that the overall performance is affected. It is possible to define the merit function so ! )(To
that it has less accuracy and/or coarser mesh resolution than meshes used for DK|/|C}6
analysis and yet produce improvements during optimization, especially in the early @QDpw1;V'
stages of a design. Bf Q#5
A rule of thumb for the first Monte Carlo run on a system is to have an average of at a,e;(/#\ 7
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays .GvZv>
on the receiver to achieve uniform distribution. It is likely that you will need to w}
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define more rays than 800 in a simulation in order to get 800 rays on the receiver. '<AE%i,
When using simplified meshes as merit functions, you should check the before and y 48zsm{
after performance of a design to verify that the changes correlate to the changes of :d@RN+U
the merit function during optimization. As a design reaches its final performance Kp6%=JjO
level, you will have to add rays to the simulation to reduce the noise floor so that /km0[M
sufficient accuracy and mesh resolution are available for the optimizer to find the GZ.KL!,R!
best solution.
